Net Model Research Articles

Research on the application of the Sleep EEG Net model based on domain adaptation transfer in the detection of driving fatigue

Fatigue detection in driving faces challenges stemming from data scarcity and difficulty in data acquisition, which poses a significant challenge to traditional fatigue detection methods. To address this issue, this study introduces a Sleep EEG Net model based on domain adaptation transfer learning. This model was pre-trained using the publicly available Sleep-EDF dataset, and domain adaptation transfer training techniques were employed to train the feature extractor of the pre-trained model, enabling cross-domain knowledge transfer. As a result, the model has been successfully applied to the task of fatigue detection in driving with only a limited amount of fatigue driving data. Experimental results demonstrate that this approach achieves a recognition accuracy of 91.5% in fatigue detection tasks. Furthermore, the model exhibits strong generalization capabilities and robustness, achieving high recognition accuracy in both simulated and real driving environments, thereby validating its effectiveness in practical applications.

Indoor/Outdoor Deep Learning Based Image Classification for Object Recognition Applications

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor sets, resulting in four trained models. The test sets are used to evaluate the trained models using many evaluation metrics (accuracy, TPR, FNR, PPR, FDR). Results of Google Net model indicate the high performance of the designed models with 99.34% and 99.76% accuracies for indoor and outdoor datasets, respectively. For Mobile Net models, the result accuracies are 99.27% and 99.68% for indoor and outdoor sets, respectively. The proposed methodology is compared with similar ones in the field of object recognition and image classification, and the comparative study proves the transcendence of the propsed system.

Optimization of safety instrumented system performance and maintenance costs in Algerian oil and gas facilities

Gas processing industry is associated with high risks which have the potential to cause catastrophic accidents. Safety Instrumented Systems (SISs) are considered the most effective safety barriers in this sector aiming to prevent undesired events and mitigate their consequences. However, several factors can affect their performance including maintenance strategy in place. In this paper, a Stochastic Petri Net (SPN) model is proposed for evaluating maintenance strategies related to SIS. The model is applied to analyze the performance of an Emergency Shutdown System (ESD) in a Flared Gases Recovery Unit located at south Algerian field. Furthermore, the paper investigates the financial impact of proof tests, including direct costs (such as manpower, equipment, and transportation) and indirect costs (such as production losses and gas flaring tax). These costs can be effectively managed and reduced by optimizing proof test intervals and scheduling tests during planned plant shutdowns. The results demonstrate that the proposed Stochastic Petri Net (SPN) model successfully analyzes the impact of imperfect full and partial proof tests on PFD average values of Safety Instrumented Functions (SIFs). In addition, it has been shown through reliability analysis that the proposed model is able to minimize spare parts expenses leading to significant cost savings while maintaining the required safety integrity levels (SILs), about 60% benefit achieved within two years compared to the actual procurement process over same period.

Deep Fraud Net: A Deep Learning Approach for Cyber Security and Financial Fraud Detection and Classification

Given the growing dependence on digital systems and the escalation of financial fraud occurrences, it is imperative to implement efficient cyber security protocols and fraud detection methodologies. The threat's dynamic nature often challenges conventional methods, necessitating the adoption of more sophisticated strategies. Individuals depend on pre-established regulations or problem-solving processes, which possess constraints in identifying novel and intricate fraudulent trends. Conventional techniques need help handling noise data and the substantial expenses incurred by false positives and true positives. To tackle these obstacles, the present study introduces Deep Fraud Net, a framework that utilizes deep learning to detect and classify instances of financial fraud and cyber security threats. The Deep Fraud Net system model entails the utilization of a deep neural network to acquire intricate patterns and characteristics from extensive datasets through training. The framework integrates noise reduction methods to enhance the precision of fraud detection and improve the quality of input data. The Deep Fraud Net method attains a precision of 98.85%, accuracy of 93.35%, sensitivity of 99.05%, specificity of 93.16%, false positive rate of 7.34%, and true positive rate of 89.58%. The findings suggest that Deep Fraud Net can effectively detect and categorize instances of fraudulent behavior with a reduced occurrence of misclassifications. The method exhibits potential implications for diverse domains that prioritize robust security and fraud detection, including but not limited to banking, e-commerce, and online transactions.

Construction of Software Supply Chain Threat Portrait Based on Chain Perspective

With the rapid growth of the software industry, the software supply chain (SSC) has become the most intricate system in the complete software life cycle, and the security threat situation is becoming increasingly severe. For the description of the SSC, the relevant research mainly focuses on the perspective of developers, lacking a comprehensive understanding of the SSC. This paper proposes a chain portrait framework of the SSC based on a resource perspective, which comprehensively depicts the threat model and threat surface indicator system of the SSC. The portrait model includes an SSC threat model and an SSC threat indicator matrix. The threat model has 3 levels and 32 dimensions and is based on a generative artificial intelligence model. The threat indicator matrix is constructed using the Attack Net model comprising 14-dimensional attack strategies and 113-dimensional attack techniques. The proposed portrait model’s effectiveness is verified through existing SSC security events, domain experts, and event visualization based on security analysis models.

Loblaw Companies Limiteds Basic Financial Analysis and Calculation Display

For a company, financial analysis can help the company analyze its financing, investment, risk management, comparison with competitors and other issues to help company decision-makers make correct decisions. For the financial market, financial analysis can help investors understand the basic characteristics, current situation and future prospects of their target investment companies and help improve social and economic stability and development. This article is mainly based on a financial analysis of the current state of Loblaw Companies Limited (hereinafter referred to as Loblaw) in 2023. The research purpose of this article is to conduct an in-depth study of Loblaw by explaining the meaning and conceptual relationship of data collection, calculations and results, so that the public can have a more comprehensive understanding of the companys financial status in 2023. This paper uses literature analysis and data analysis research methods to mainly explore Loblaws risks, financing costs, current capital plans and capital structure. The data analysis models used include linear regression model, dividend discount model (DDM), capital asset pricing model (CAPM), weighted average cost of capital (WACC), net present value model (NPV). The data used for calculation comes from authoritative websites such as Yahoo Finance, Bank of Canada, and Loblaws official website. The results of the study found that Loblaw has a dominant position in the Canadian retail market. Loblaw is one company that relies more on debt financing. Its financing costs are lower than the industry average. Its capital structure has been very stable in recent years. The market has full confidence in Loblaw.

Enhancing airport environmental sustainability through net zero energy building principles

Airports play a crucial role in Indonesia, an archipelagic country, by connecting various regions, especially those separated by vast oceans. The extensive scale of airport construction directly influences environmental conditions, making environmental preservation and sustainability essential considerations. Hence, implementing the net zero energy building principle in airports, particularly in Indonesia, becomes imperative to enhance environmental sustainability. This study focused on Yogyakarta International Airport (YIA), a facility that has achieved Greenship Gold certification, making it a suitable case for investigating the application of net zero energy building principles. Adapting the net zero building concept to complex airport structures presents challenges. Besides using rating tools, simulation modeling offers a performance-based approach to assess the application of net zero energy building principles effectively. We used quantitative method by utilizing software like EDGE to simulate net zero energy buildings and identify optimal performance strategies for YIA. The study’s outcomes comprised an analysis of the net zero energy building model, providing valuable insights for developing specific guidelines on net zero energy principles in airport terminal buildings. This research aimed to expand knowledge on net zero building performance at airports, contributing to the development of guidelines for net zero airport terminal criteria.

Proteomic‐based Biological Aging Clock and MRI Markers of Cerebrovascular Disease: Atherosclerosis Risk in Community Study

AbstractBackgroundBiological aging state can be quantified by composite metrics called aging clocks using proteomics. Proteomic aging clocks (PACs) are accurate, easily measured, and are associated with age‐related diseases including Alzheimer’s Disease and Related Dementias. We aim to investigate whether an accelerated biological aging (a discrepancy between chronological age and PAC) is associated with subclinical cerebrovascular structural changes.Method1494 participants from the Atherosclerosis Risk in Communities (ARIC) Study with proteomics and 3T brain MRI data at ARIC visit 5 in 2011‐13 (mean age 76, 59% female, 25% Black) were included. Nearly 5000 plasma proteins were measured using the SomaScan assay. PAC was developed using elastic net regression model and was internally validated. Age acceleration was calculated as residuals after regressing PAC on chronological age (positive value indicates biological age is higher than the person’s chronological age). Linear and logistic regression models were used to assess the associations of age acceleration with white matter hyperintensity volume (in cm3, log2 transformed, median [IQR]: 11[6‐20]) and the presence of: subcortical (n = 281), lacunar (n = 263), and cortical infarcts (n = 150), and microbleeds (n = 355).ResultAccelerated age (median [IQR]:‐0.04[‐1.4,1.3]; correlation with chronological age = 0) was associated with MRI markers of cerebrovascular disease after adjusting for demographic, cardiovascular risk factors, education, and kidney function. Every five years higher age acceleration was associated with larger white matter hyperintensity volume (Difference:0.34[95%CI 0.19,0.49]), higher odds of lacunar (OR:1.61[1.13,2.30]), subcortical (OR:1.63[1.15, 2.32]), and cortical infarcts (OR:1.72[1.11,2.67]). There was no association with presence of microbleeds (OR:1.25[0.91,1.73]) (Figure). Findings were consistent after excluding participants with clinical stroke and there was no difference between APOEe4+ and APOEe4‐ participants.ConclusionHigher accelerated age is cross‐sectionally associated with a greater prevalence of MRI markers of cerebrovascular disease. Understanding this relation has potential to help with risk stratification and personalized prevention and treatment strategies to promote brain health.

VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria

This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.

A novel fault detection and diagnostic Petri net methodology for dynamic systems

Faults can have significant, negative impacts on the operation and performance of simple and complex dynamic systems. Based on the integration of Bayesian network diagnostic features with Petri net formalism, the existing Bayesian-supported Petri net tool has demonstrated the flexibility of using the Petri net approach for diagnosing failure scenario of a dynamic system. However, studies on using the proposed hybrid Petri net approach for condition monitoring and early detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops are yet to be investigated. Thus, this paper presents a methodology to address this research gap using the operation of a water tank level control system as a case study. The method combines the constructed Generalised Stochastic Petri Net (GSPN) model of the system operation with its corresponding fault diagnostic Petri net model, created using the proposed modified Bayesian Stochastic Petri Net (mBSPN) formalism. The GSPN model establishes the causal relationships between the system’s components and/or subsystems. It further identifies deviations in the sensor measurements of the observable process variables characterising the system operation. The information provided by the sensors in the system model are then inputted into the mBSPN model to diagnose the root cause of the observed deviations. The obtained results demonstrated the capability of using the proposed integrated Petri net methodology for system condition monitoring, early fault detection and diagnosis of single and multiple failures in a dynamic system with feedback control loops.

Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol.

In low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility. This prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5-10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values. The current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. https://clinicaltrials.gov/, identifier (NCT05730387).

Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study.

Rapid digitalization in health care has led to the adoption of digital technologies; however, limited trust in internet-based health decisions and the need for technical personnel hinder the use of smartphones and machine learning applications. To address this, automated machine learning (AutoML) is a promising tool that can empower health care professionals to enhance the effectiveness of mobile health apps. We used AutoML to analyze data from clinical studies involving patients with chronic hand and/or foot eczema or psoriasis vulgaris who used a smartphone monitoring app. The analysis focused on itching, pain, Dermatology Life Quality Index (DLQI) development, and app use. After extensive data set preparation, which consisted of combining 3 primary data sets by extracting common features and by computing new features, a new pseudonymized secondary data set with a total of 368 patients was created. Next, multiple machine learning classification models were built during AutoML processing, with the most accurate models ultimately selected for further data set analysis. Itching development for 6 months was accurately modeled using the light gradient boosted trees classifier model (log loss: 0.9302 for validation, 1.0193 for cross-validation, and 0.9167 for holdout). Pain development for 6 months was assessed using the random forest classifier model (log loss: 1.1799 for validation, 1.1561 for cross-validation, and 1.0976 for holdout). Then, the random forest classifier model (log loss: 1.3670 for validation, 1.4354 for cross-validation, and 1.3974 for holdout) was used again to estimate the DLQI development for 6 months. Finally, app use was analyzed using an elastic net blender model (area under the curve: 0.6567 for validation, 0.6207 for cross-validation, and 0.7232 for holdout). Influential feature correlations were identified, including BMI, age, disease activity, DLQI, and Hospital Anxiety and Depression Scale-Anxiety scores at follow-up. App use increased with BMI >35, was less common in patients aged >47 years and those aged 23 to 31 years, and was more common in those with higher disease activity. A Hospital Anxiety and Depression Scale-Anxiety score >8 had a slightly positive effect on app use. This study provides valuable insights into the relationship between data characteristics and targeted outcomes in patients with chronic eczema or psoriasis, highlighting the potential of smartphone and AutoML techniques in improving chronic disease management and patient care.

Risk factors for hyperglycemia in COVID-19 patients treated with remdesivir.

The primary objective of this study was to investigate the factors contributing to hyperglycemic adverse events (AEs) associated with the administration of remdesivir in hospitalized patients diagnosed with coronavirus disease 2019 (COVID-19). Furthermore, the study aimed to develop a risk score model employing various machine learning approaches. A total of 1262 patients were enrolled in this investigation. The relationship between covariates and hyperglycemic AEs was assessed through logistic regression analysis. Diverse machine learning algorithms were employed for the purpose of forecasting hyperglycemia-related complications. After adjusting for covariates, individuals with a body mass index ≥23 kg/m2 , those using proton pump inhibitors, cholinergic medications, or individuals with cardiovascular diseases exhibited approximately 2.41-, 2.73-, 2.65-, and 1.97-fold higher risks of experiencing hyperglycemic AEs (95% CI 1.271-4.577, 1.223-6.081, 1.168-5.989, and 1.119-3.472, respectively). Multivariate logistic regression, elastic net, and random forest models displayed area under the receiver operating characteristic curve values of 0.65, 0.66, and 0.60, respectively (95% CI 0.572-0.719, 0.640-0.671, and 0.583-0.611, respectively). This study comprehensively explored factors associated with hyperglycemic complications arising from remdesivir administration and, concurrently, leveraged a range of machine learning methodologies to construct a risk scoring model, thereby facilitating the tailoring of individualized remdesivir treatment regimens for patients with COVID-19.

Generalized Net Model of Heavy Oil Products’ Manufacturing in Petroleum Refinery

Generalized Net Model of Heavy Oil Products’ Manufacturing in Petroleum Refinery

Prediction of patient-specific quality assurance for volumetric modulated arc therapy using radiomics-based machine learning with dose distribution.

We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom. A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient. The RMSE and MAE for the developed machine learning models ranged from<1% to nearly<10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom. The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics.

Weed detection in agricultural fields via automatic graph cut segmentation with Mobile Net classification model

Weed detection in agricultural fields via automatic graph cut segmentation with Mobile Net classification model

Prognostic Value of 18F-FDG PET Radiomics Features at Baseline in PET-Guided Consolidation Strategy in Diffuse Large B-Cell Lymphoma: A Machine-Learning Analysis from the GAINED Study.

The results of the GA in Newly Diagnosed Diffuse Large B-Cell Lymphoma (GAINED) study demonstrated the success of an 18F-FDG PET-driven approach to allow early identification-for intensification therapy-of diffuse large B-cell lymphoma patients with a high risk of relapse. Besides, some works have reported the prognostic value of baseline PET radiomics features (RFs). This work investigated the added value of such biomarkers on survival of patients involved in the GAINED protocol. Methods: Conventional PET features and RFs were computed from 18F-FDG PET at baseline and extracted using different volume definitions (patient level, largest lesion, and hottest lesion). Clinical features and the consolidation treatment information were also considered in the model. Two machine-learning pipelines were trained with 80% of patients and tested on the remaining 20%. The training was repeated 100 times to highlight the test set variability. For the 2-y progression-free survival (PFS) outcome, the pipeline included a data augmentation and an elastic net logistic regression model. Results for different feature groups were compared using the mean area under the curve (AUC). For the survival outcome, the pipeline included a Cox univariate model to select the features. Then, the model included a split between high- and low-risk patients using the median of a regression score based on the coefficients of a penalized Cox multivariate approach. The log-rank test P values over the 100 loops were compared with a Wilcoxon signed-ranked test. Results: In total, 545 patients were included for the 2-y PFS classification and 561 for survival analysis. Clinical features alone, consolidation features alone, conventional PET features, and RFs extracted at patient level achieved an AUC of, respectively, 0.65 ± 0.07, 0.64 ± 0.06, 0.60 ± 0.07, and 0.62 ± 0.07 (0.62 ± 0.07 for the largest lesion and 0.54 ± 0.07 for the hottest). Combining clinical features with the consolidation features led to the best AUC (0.72 ± 0.06). Adding conventional PET features or RFs did not improve the results. For survival, the log-rank P values of the model involving clinical and consolidation features together were significantly smaller than all combined-feature groups (P < 0.007). Conclusion: The results showed that a concatenation of multimodal features coupled with a simple machine-learning model does not seem to improve the results in terms of 2-y PFS classification and PFS prediction for patient treated according to the GAINED protocol.

Analyzing the decision-making process of international trade agreements using Petri nets

Abstract This paper aims to analyze regulatory cooperation in international trade agreements by combining petri net models with intuitionistic fuzzy petri net models. A security information system for international trade agreements combines formal mathematical methods with visual graphics. The correlation matrix and invariants method is utilized to analyze invariants in international trade agreements. The correlation matrix reorganization algorithm is employed to examine correlation factors in the decision-making process of international trade agreements. The results show that the fuzzy Petri net model has the least fluctuation in analyzing the information security posture value of international trade agreements, and the security posture value floats between 6.7 and 7.3. The newest ITA regulation is 0.2 higher than the traditional regulation in consistency, the embodiment of the newest ITA regulation in democracy is 0.75, and the transparency in the newest ITA regulation is increased by 0.3. This paper’s research enhances the useful data and information for analyzing the factors influencing ITAs’ decision-making.

Abstract 17801: Age-Specific Normal Values for Myocardial Blood Flow on 82rb Positron Emission Tomography

Introduction: Positron emission tomography (PET) myocardial perfusion imaging (MPI) is utilized to evaluate ischemia along with myocardial blood flow (MBF). The ability to adequately augment blood flow, measured by myocardial blood flow reserve (MBFR), is a measure of overall health of the coronary circulation and associated with lower major adverse cardiovascular events (MACE) and all-cause mortality. The age-specific ranges of MBFR in patients without demonstrable coronary artery disease (CAD) has not been well established. Hypothesis/Aim: To determine the effect of age on MBF and MBFR in a cohort of patients without demonstrable CAD. Methods: Patients who underwent PET MPI studies from 2012-2022 on Siemens PET/CT cameras were included if Summed Stress Score=0, coronary calcium score=0, and left ventricular ejection fraction ≥50%. Those with prior history of MI or coronary intervention, heart/kidney/liver transplant, cirrhosis, or CKD stage IV+ were excluded. MBF was calculated using a net retention model (ImagenQ, Cardiovascular Imaging Technologies, Kansas City, MO) and multivariable linear regression models were developed to predict MBF. Results: Among 3,235 patients (age 59.5+/-12.6 years, 24.2% male) with median rest MBF 0.74 [0.61-0.92] and stress MBF 1.71 [1.41-2.10], MBFR was 2.28 [1.93-2.72]. With increasing age, rest MBF remained fairly stable while stress MBF and MBFR decreased, such that for every 10 years of age, stress MBF decreased by 0.07 [95% CI 0.05-0.08] mL/min/g and MBFR decreased by 0.12 [95% CI 0.11-0.14] after multivariable adjustment. Age-specific predicted MBFR values are documented in the Table. Conclusions: In patients without demonstrable CAD, the MBFR decreases with age. Abnormal MBFR values under 2.0 may be seen in a significant number of older patients.

Balanced Fuzzy Petri Nets

The paper presents a new Petri Net model: BFPN. Balanced Fuzzy Petri Nets make it possible to model processes that consider gain and loss calculations at the same time. Many business processes need to consider the ability to model a scarcity or loss situation. Balanced fuzzy sets and relations allow a complete visualization and interpretation of the processes.